144 datasets found

Brain tumour MRI segmentation dataset

Knee OsteoArthritis (KOA) dataset used for early detection of KOA (KL-0 vs KL-2) using Vision Transformer (ViT) model with selective shuffled position embedding and key-patch...

In-vivo fully sampled T2-weighted k-space data from MR scanner

Fully sampled multi-coil k-space data from the scanner, retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data

A dataset for testing the proposed method for sparse spherical deconvolution.

A dataset for testing the proposed method for sparse spherical deconvolution.

Bladder tumor dataset contains 2200 MRI slices from 25 patients with pathologically confirmed bladder cancer.

The dataset used in this paper is a simulated motion dataset for MRI scans, created to test the proposed motion correction method.

The ACDC dataset is a large-scale dataset containing images of urban scenes under different weather conditions.

The dataset consists of T1-weighted brain MRI images of adults, with manual segmentation of anatomical structures provided.

Dataset for IDH status determination from MRI

Dataset for prostate cancer detection from multiparametric MRI

Multi-modality MRI dataset for brain tumor segmentation

The Human Connectome Project (HCP) dataset, containing cortical surface meshes and metrics (sulcal depth, curvature, cortical thickness and T1w/T2w myelination) derived from T1-...

The dataset used for training and testing the proposed (v)sMAE model, containing cortical surface meshes and metrics (sulcal depth, curvature, cortical thickness and T1w/T2w...

The dataset contains prostate MRI and ultrasound images with pathology and coordinates of tracked biopsy.

Multi-parametric MRI scans from 2000 patients were used for BraTS2021, 1251 of which were provided with segmentation labels to the participants for developing their algorithms,...

The dataset contains 293 HGG and 76 LGG pre-operative scans in four MRI modalities, which are T1, T2, T1c and FLAIR.

Low-field MRI images were synthesised to have a resolution of 1.5mm×1.5mm×1.5mm and added Rician noise to emulate a low SNR of 70mT

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...